Finding out: a system for providing rapid and reliable answers to questions in the construction sector
Bibliographic record
Abstract
The construction sector is notorious for the dichotomy between its intensive use of information in its decision‐making processes and its limited access to, and insufficient use of, the pertinent information that is potentially available, e.g. on the internet. This paper seeks to examine this issue. To solve this problem (the ‘problem of information aboutinformation’), a multidisciplinary team developed an online question‐answering (Q.‐A.)system that uses natural language for the query and the reply. The system provides a direct answer to questions posed by building industry participants, instead of providing a list of references (as is the case with most online information retrieval systems), much as if onewere asking a question of, and receiving a response from, an expert.It has the capabilitiesto process questions in natural language, to find appropriate fragments of answers indifferent web sites and to condense them into a paragraph, also written in natural language. The main features of the system are that it uses domain‐specific knowledge (in the form ofa hierarchical specialized thesaurus complemented by terms of fieldwork parlance),semantic categorization, a database of filtered and indexed web sites, and an online interface that is adapted to different profiles of actors in the construction sector. The testing process shows that the system goes beyond the lists of references and links provided by traditional search engines on the web.The Q.‐A.system already gives 70% of satisfactory answers. The Q.‐A.system can be applied to other business domains apart from information retrieval and decision‐making in the building sector. It is also possible to apply it to the exploitation of in‐house knowledge management database.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.037 | 0.020 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".